| dc.creator | Loukas E.P., Bodurri K., Evangelopoulos P., Bouhouras A.S., Poulakis N., Christoforidis G.C., Panapakidis I., Chatzisavvas K.C. | en |
| dc.date.accessioned | 2023-01-31T08:55:26Z | |
| dc.date.available | 2023-01-31T08:55:26Z | |
| dc.date.issued | 2019 | |
| dc.identifier | 10.1109/MPS.2019.8759666 | |
| dc.identifier.isbn | 9781728107509 | |
| dc.identifier.uri | http://hdl.handle.net/11615/76008 | |
| dc.description.abstract | This paper examines the application of machine learning techniques in NILM methodologies based on the first three odd harmonic order current vectors as the only attributes of the appliances. Proper formulation of the measured current waveform of appliances' combinations is also presented. We apply our methodology on performed measurements of typical Low Voltage residential installations considering harmonic order currents as the input features for both the training and disaggregation scheme. Our results support the hypothesis that the identification performance is enhanced when higher harmonic currents are included in the NILM methodology. © 2019 IEEE. | en |
| dc.language.iso | en | en |
| dc.source | Proceedings of 2019 8th International Conference on Modern Power Systems, MPS 2019 | en |
| dc.source.uri | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85070264775&doi=10.1109%2fMPS.2019.8759666&partnerID=40&md5=59361e30bd70fc6ff8b2f5a63a3a4eb0 | |
| dc.subject | Harmonic analysis | en |
| dc.subject | Learning systems | en |
| dc.subject | Current vectors | en |
| dc.subject | Harmonic currents | en |
| dc.subject | Higher harmonics | en |
| dc.subject | Load identification | en |
| dc.subject | Machine learning approaches | en |
| dc.subject | Machine learning techniques | en |
| dc.subject | Measured currents | en |
| dc.subject | NILM | en |
| dc.subject | Machine learning | en |
| dc.subject | Institute of Electrical and Electronics Engineers Inc. | en |
| dc.title | A Machine Learning Approach for NILM based on Odd Harmonic Current Vectors | en |
| dc.type | conferenceItem | en |
